POS0351 MULTI-OMICS ANALYSIS IDENTIFIES DIFFERENTIAL LIPID REGULATION AS A POTENTIAL MECHANISM UNDERPINNING METHOTREXATE RESPONSE IN RHEUMATOID ARTHRITIS
Authors: C. F. Yap, N. Nair, S. Verstappen, K. Hyrich, A. Barton, D. Plant
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes inflammation and joint damage, leading to disability and reduced quality of life. Methotrexate (MTX) is a widely used disease-modifying anti-rheumatic drug that is initially prescribed to reduce inflammation and slowing disease progression in RA; however, approximately 40% of treated patients do not experience a satisfactory response to this drug. Identifying reliable surrogate biomarkers of MTX response would facilitate precision medicine strategies and improve patient outcomes. <h3>Objectives</h3> To identify blood-based transcriptomic and metabolomic biomarkers of MTX response in patients with RA. <h3>Methods</h3> Whole blood samples were taken from patients taking part in the Rheumatoid Arthritis Medication Study (RAMS), an observational longitudinal cohort of early RA patients starting MTX for the first time in the UK. RNA-seq data was generated using the Illumina NovaSeq 6000 platform of patients before and after 4 weeks MTX treatment. Treatment response was determined after 6 months on drug and patients were classified as good or non-responders using established EULAR response criteria. Differential gene expression analysis was performed using glmmSeq [1], which uses generalized linear mixed model with negative binomial distribution. Models were parameterized with size factor and dispersion estimated using DESeq2 [2]. The regression model included patient ID as a random effect and an interaction term was included between time-point and response category. Two-hundred and fifty targeted metabolites were quantified in serum samples from the same patients as above using nuclear magnetic resonance (NMR) based technology. Metabolomics data were normalised using probabilistic quotient normalization and scaled with pareto-scaling. Partial least-square regression (PLSR) and linear mixed effects models (constructed as above) were used to identify differentially expressed metabolites. All linear mixed models were adjusted for baseline disease activity (DAS28). Enrichment analysis for differentially expressed genes was performed using Metascape [3]. <h3>Results</h3> In total, 100 patients were analysed (69 good-responders and 31 non-responders). Following quality control, 17,298 genes were tested for differential expression, 25 were significantly associated with response (fold change ≥ 1.5 at baseline, adjusted p-value &lt; 0.05). Of these, 4 were enriched in monocarboxylic acid (which includes fatty acid) metabolism. Differential regulation of this biological process was also observed in the metabolomics profile where low-density lipoprotein cholesterol and triglycerides were associated with MTX treatment response; this was observed using both PLSR (AUC: 0.75) and linear mixed effects models (p-value &lt; 0.05). <h3>Conclusion</h3> Whole blood multi-omics analysis has the potential to identify biomarkers and biological processes underpinning MTX response in RA. Further research is needed to understand and confirm the relationship between monocarboxylic acid metabolism and MTX response in this disease. <h3>References</h3> [1]M L, K G, E S. glmmSeq: General Linear Mixed Models for Gene-Level Differential Expression. 2022.https://myles-lewis.github.io/glmmSeq/, (accessed 5 Dec 2022). [2]Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. <i>Genome Biol</i> 2014;<b>15</b>:550. doi:10.1186/s13059-014-0550-8 [3]Zhou Y, Zhou B, Pache L, <i>et al.</i> Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. <i>Nat Commun</i> 2019;<b>10</b>:1523. doi:10.1038/s41467-019-09234-6 <h3>Acknowledgements</h3> We would like to 3TR and CNAG for generating the transcriptomics dataset. <h3>Disclosure of Interests</h3> Chuan Fu Yap: None declared, Nisha Nair: None declared, Suzanne Verstappen: None declared, Kimme Hyrich Speakers bureau: Abbvie, Grant/research support from: Pfizer, Bristol Myers Squibb, Anne Barton Speakers bureau: Galapagos, Grant/research support from: Pfizer, Bristol Myers Squibb, Scipher Medicine, Galapagos, Darren Plant Grant/research support from: Bristoly Myers Squibb.